Major Description
Large graphs may not fit entirely in memory. If we supported custom storage levels for the vertices and edges of a graph, the user could specify MEMORY_AND_DISK and then repartition the graph to use many small partitions, each of which does fit in memory. Spark would then automatically load partitions from disk as needed.
Also, the replicated storage levels would be helpful for fault tolerance, and the serialized ones would improve efficiency for non-primitive vertex and edge attributes.